Andrew B. Lawson is a professor of biostatistics in the Division of Biostatistics, Department of Public Health Sciences, College of Medicine at the Medical University of South Carolina (MUSC). He is an MUSC eminent scholar and American Statistical Association (ASA) fellow. He is also an advisor in disease mapping and risk assessment for the World Health Organization, the founding editor of the journal Spatial and Spatio-Temporal Epidemiology, and the author of eight books, including the highly regarded Chapman & Hall/CRC book Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. He has published more than 150 journal articles on spatial epidemiology, spatial statistics, and related areas. He earned a PhD in spatial statistics from the University of St. Andrews.Sudipto Banerjee is a professor and chair in the Department of Biostatistics at the University of California, Los Angeles. He is an elected fellow of the ASA, the Institute of Mathematical Statistics, and the International Statistical Institute. He is also a recipient of the Mortimer Spiegelman Award from the American Public Health Association. He is the author/coauthor of more than 100 peer-reviewed publications and two highly regarded Chapman & Hall/CRC books: Hierarchical Modeling and Analysis for Spatial Data, Second Edition and Linear Algebra and Matrix Analysis for Statistics. His research interests include hierarchical modeling and Bayesian inference for spatially referenced data.Robert Haining retired as a professor of human geography from the University of Cambridge in September 2015. He is the author/coauthor of more than 150 articles and two books. His research focuses on the quantitative analysis of geographical data, including the geography of health, spatial representation, spatial sampling, exploratory data analysis, small-area estimation and hypothesis testing, spatial data analysis, and spatial econometrics. His past work has involved the evaluation of the impact of air pollution on health status using small-area statistics as well as the development of new methods for evaluating the effectiveness of small-area targeted police interventions.María Dolores Ugarte is a professor of statistics at the Public University of Navarre. She is the author/coauthor of many papers on statistics and epidemiology and several books, including the recent Chapman & Hall/CRC book Probability and Statistics with R, Second Edition. She is also an associate editor for Statistical Modelling, TEST, and Computational Statistics and Data Analysis as well as an editorial panel member of Spatial and Spatio-Temporal Epidemiology. Her research focuses on spatiotemporal disease mapping and small-area estimation with applications in several fields. She earned a PhD in statistics from the Public University of Navarre.

"In 2008, CRC Press started publishing the Handbooks of Modern Statistical Methods. Apparently the series is popular as it is growing rapidly, with 13 volumes printed now and 7 announced. It is easy to understand why: the books are attractive in content, presentation and price. The present volume is no exception. The book has been edited by first-class experts, who also contributed a number of chapters. The book’s website gives a table of the contents of the 35 chapters. It documents the rich variety of subjects. … I can only recommend this book."—Paul Eilers, ISCB News, May 2017

• Part I: Introduction Chapter 1: Integration of Different Epidemiologic Perspectives and Applications to Spatial Epidemiology Sara Wagner Robb, Sarah E. Bauer, John E. Vena Chapter 2: Environmental Studies Mark J. Nieuwenhuijsen Chapter 3: Interpreting Clusters of Health Events Geoffrey Jacquez, Jared Aldstadt Chapter 4: Geographic Information Systems in Spatial Epidemiology and Public Health Robert Haining, Ravi Maheswaran Chapter 5: Ecological Modeling: General Issues Jon C. Wakefield, Theresa R. SmithPart II: Basic Methods Chapter 6: Case Event and Count Data Modeling Andrew B. Lawson Chapter 7: Bayesian Modeling and Inference Georgiana Onicescu, Andrew B. Lawson Chapter 8: Statistical Tests for Clustering and Surveillance Peter A. Rogerson, Geoffrey Jacquez Chapter 9: Scan Tests Inkyung Jung Chapter 10: Kernel Smoothing Methods Martin L. HazeltonPart III: Special Methods Chapter 11: Geostatistics in Small-Area Health Applications Patrick E. Brown Chapter 12: Splines in Disease Mapping Tomás Goicoa, Jaione Etxeberria, and María Dolores Ugarte Chapter 13: Quantile Regression for Epidemiological Applications Brian J. Reich Chapter 14: Focused Clustering: Statistical Analysis of Spatial Patterns of Disease around Putative Sources of Increased Risk Lance A. Waller, David C. Wheeler, Jeffrey M. Switchenko Chapter 15: Estimating the Health Impact of Air Pollution Fields Duncan Lee, Sujit K. Sahu Chapter 16: Data Assimilation for Environmental Pollution Fields Howard H. Chang Chapter 17: Spatial Survival Models Sudipto Banerjee Chapter 18: Spatial Longitudinal Analysis Andrew B. Lawson Chapter 19: Spatiotemporal Disease Mapping Andrew B. Lawson, Jungsoon Choi Chapter 20: Mixtures and Latent Structure in Spatial Epidemiology Md. Monir Hossain and Andrew B. Lawson Chapter 21: Bayesian Nonparametric Modeling for Disease Incidence Data Athanasios Kottas Chapter 22: Multivariate Spatial Models Sudipto BanerjeePart IV: Special Problems and Applications Chapter 23: Bayesian Variable Selection in Semiparametric and Nonstationary Geostatistical Models: An Application to Mapping Malaria Risk in Mali Federica Giardina, Nafomon Sogoba, Penelope Vounatsou Chapter 24: Computational Issues and R Packages for Spatial Data Analysis Marta Blangiardo, Michela Cameletti Chapter 25: The Role of Spatial Analysis in Risk-Based Animal Disease Management Kim B. Stevens, Dirk U. Pfeiffer Chapter 26: Infectious Disease Modelling Michael Höhle Chapter 27: Spatial Health Surveillance Ana Corberán-Vallet and Andrew B. Lawson Chapter 28: Cluster Modeling and Detection Andrew B. Lawson Chapter 29: Spatial Data Analysis for Health Services Research Brian Neelon Chapter 30: Spatial Health Survey Data Christel Faes, Yannick Vandendijck, Andrew B. Lawson Chapter 31: Graphical Modeling of Spatial Health Data Adrian Dobra Chapter 32: Smoothed ANOVA Modeling Miguel A. Martinez-Beneito, James S. Hodges, and Marc Marí-Dell’Olmo Chapter 33: Sociospatial Epidemiology: Segregation Sue C. Grady Chapter 34: Sociospatial Epidemiology: Residential History Analysis David C. Wheeler, Catherine A. Calder Chapter 35: Spatiotemporal Modeling of Preterm Birth Joshua L. Warren, Montserrat Fuentes, Amy H. Herring, Peter H. Langlois

Andrew B. Lawson is a professor of biostatistics in the Division of Biostatistics, Department of Public Health Sciences, College of Medicine at the Medical University of South Carolina (MUSC). He is an MUSC eminent scholar and American Statistical Association (ASA) fellow. He is also an advisor in disease mapping and risk assessment for the World Health Organization, the founding editor of the journal Spatial and Spatio-Temporal Epidemiology, and the author of eight books, including the highly regarded Chapman & Hall/CRC book Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. He has published more than 150 journal articles on spatial epidemiology, spatial statistics, and related areas. He earned a PhD in spatial statistics from the University of St. Andrews.Sudipto Banerjee is a professor and chair in the Department of Biostatistics at the University of California, Los Angeles. He is an elected fellow of the ASA, the Institute of Mathematical Statistics, and the International Statistical Institute. He is also a recipient of the Mortimer Spiegelman Award from the American Public Health Association. He is the author/coauthor of more than 100 peer-reviewed publications and two highly regarded Chapman & Hall/CRC books: Hierarchical Modeling and Analysis for Spatial Data, Second Edition and Linear Algebra and Matrix Analysis for Statistics. His research interests include hierarchical modeling and Bayesian inference for spatially referenced data.Robert Haining retired as a professor of human geography from the University of Cambridge in September 2015. He is the author/coauthor of more than 150 articles and two books. His research focuses on the quantitative analysis of geographical data, including the geography of health, spatial representation, spatial sampling, exploratory data analysis, small-area estimation and hypothesis testing, spatial data analysis, and spatial econometrics. His past work has involved the evaluation of the impact of air pollution on health status using small-area statistics as well as the development of new methods for evaluating the effectiveness of small-area targeted police interventions.María Dolores Ugarte is a professor of statistics at the Public University of Navarre. She is the author/coauthor of many papers on statistics and epidemiology and several books, including the recent Chapman & Hall/CRC book Probability and Statistics with R, Second Edition. She is also an associate editor for Statistical Modelling, TEST, and Computational Statistics and Data Analysis as well as an editorial panel member of Spatial and Spatio-Temporal Epidemiology. Her research focuses on spatiotemporal disease mapping and small-area estimation with applications in several fields. She earned a PhD in statistics from the Public University of Navarre.

"In 2008, CRC Press started publishing the Handbooks of Modern Statistical Methods. Apparently the series is popular as it is growing rapidly, with 13 volumes printed now and 7 announced. It is easy to understand why: the books are attractive in content, presentation and price. The present volume is no exception. The book has been edited by first-class experts, who also contributed a number of chapters. The book’s website gives a table of the contents of the 35 chapters. It documents the rich variety of subjects. … I can only recommend this book."—Paul Eilers, ISCB News, May 2017

• Part I: Introduction Chapter 1: Integration of Different Epidemiologic Perspectives and Applications to Spatial Epidemiology Sara Wagner Robb, Sarah E. Bauer, John E. Vena Chapter 2: Environmental Studies Mark J. Nieuwenhuijsen Chapter 3: Interpreting Clusters of Health Events Geoffrey Jacquez, Jared Aldstadt Chapter 4: Geographic Information Systems in Spatial Epidemiology and Public Health Robert Haining, Ravi Maheswaran Chapter 5: Ecological Modeling: General Issues Jon C. Wakefield, Theresa R. SmithPart II: Basic Methods Chapter 6: Case Event and Count Data Modeling Andrew B. Lawson Chapter 7: Bayesian Modeling and Inference Georgiana Onicescu, Andrew B. Lawson Chapter 8: Statistical Tests for Clustering and Surveillance Peter A. Rogerson, Geoffrey Jacquez Chapter 9: Scan Tests Inkyung Jung Chapter 10: Kernel Smoothing Methods Martin L. HazeltonPart III: Special Methods Chapter 11: Geostatistics in Small-Area Health Applications Patrick E. Brown Chapter 12: Splines in Disease Mapping Tomás Goicoa, Jaione Etxeberria, and María Dolores Ugarte Chapter 13: Quantile Regression for Epidemiological Applications Brian J. Reich Chapter 14: Focused Clustering: Statistical Analysis of Spatial Patterns of Disease around Putative Sources of Increased Risk Lance A. Waller, David C. Wheeler, Jeffrey M. Switchenko Chapter 15: Estimating the Health Impact of Air Pollution Fields Duncan Lee, Sujit K. Sahu Chapter 16: Data Assimilation for Environmental Pollution Fields Howard H. Chang Chapter 17: Spatial Survival Models Sudipto Banerjee Chapter 18: Spatial Longitudinal Analysis Andrew B. Lawson Chapter 19: Spatiotemporal Disease Mapping Andrew B. Lawson, Jungsoon Choi Chapter 20: Mixtures and Latent Structure in Spatial Epidemiology Md. Monir Hossain and Andrew B. Lawson Chapter 21: Bayesian Nonparametric Modeling for Disease Incidence Data Athanasios Kottas Chapter 22: Multivariate Spatial Models Sudipto BanerjeePart IV: Special Problems and Applications Chapter 23: Bayesian Variable Selection in Semiparametric and Nonstationary Geostatistical Models: An Application to Mapping Malaria Risk in Mali Federica Giardina, Nafomon Sogoba, Penelope Vounatsou Chapter 24: Computational Issues and R Packages for Spatial Data Analysis Marta Blangiardo, Michela Cameletti Chapter 25: The Role of Spatial Analysis in Risk-Based Animal Disease Management Kim B. Stevens, Dirk U. Pfeiffer Chapter 26: Infectious Disease Modelling Michael Höhle Chapter 27: Spatial Health Surveillance Ana Corberán-Vallet and Andrew B. Lawson Chapter 28: Cluster Modeling and Detection Andrew B. Lawson Chapter 29: Spatial Data Analysis for Health Services Research Brian Neelon Chapter 30: Spatial Health Survey Data Christel Faes, Yannick Vandendijck, Andrew B. Lawson Chapter 31: Graphical Modeling of Spatial Health Data Adrian Dobra Chapter 32: Smoothed ANOVA Modeling Miguel A. Martinez-Beneito, James S. Hodges, and Marc Marí-Dell’Olmo Chapter 33: Sociospatial Epidemiology: Segregation Sue C. Grady Chapter 34: Sociospatial Epidemiology: Residential History Analysis David C. Wheeler, Catherine A. Calder Chapter 35: Spatiotemporal Modeling of Preterm Birth Joshua L. Warren, Montserrat Fuentes, Amy H. Herring, Peter H. Langlois

Andrew B. Lawson is a professor of biostatistics in the Division of Biostatistics, Department of Public Health Sciences, College of Medicine at the Medical University of South Carolina (MUSC). He is an MUSC eminent scholar and American Statistical Association (ASA) fellow. He is also an advisor in disease mapping and risk assessment for the World Health Organization, the founding editor of the journal Spatial and Spatio-Temporal Epidemiology, and the author of eight books, including the highly regarded Chapman & Hall/CRC book Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. He has published more than 150 journal articles on spatial epidemiology, spatial statistics, and related areas. He earned a PhD in spatial statistics from the University of St. Andrews.Sudipto Banerjee is a professor and chair in the Department of Biostatistics at the University of California, Los Angeles. He is an elected fellow of the ASA, the Institute of Mathematical Statistics, and the International Statistical Institute. He is also a recipient of the Mortimer Spiegelman Award from the American Public Health Association. He is the author/coauthor of more than 100 peer-reviewed publications and two highly regarded Chapman & Hall/CRC books: Hierarchical Modeling and Analysis for Spatial Data, Second Edition and Linear Algebra and Matrix Analysis for Statistics. His research interests include hierarchical modeling and Bayesian inference for spatially referenced data.Robert Haining retired as a professor of human geography from the University of Cambridge in September 2015. He is the author/coauthor of more than 150 articles and two books. His research focuses on the quantitative analysis of geographical data, including the geography of health, spatial representation, spatial sampling, exploratory data analysis, small-area estimation and hypothesis testing, spatial data analysis, and spatial econometrics. His past work has involved the evaluation of the impact of air pollution on health status using small-area statistics as well as the development of new methods for evaluating the effectiveness of small-area targeted police interventions.María Dolores Ugarte is a professor of statistics at the Public University of Navarre. She is the author/coauthor of many papers on statistics and epidemiology and several books, including the recent Chapman & Hall/CRC book Probability and Statistics with R, Second Edition. She is also an associate editor for Statistical Modelling, TEST, and Computational Statistics and Data Analysis as well as an editorial panel member of Spatial and Spatio-Temporal Epidemiology. Her research focuses on spatiotemporal disease mapping and small-area estimation with applications in several fields. She earned a PhD in statistics from the Public University of Navarre.

"In 2008, CRC Press started publishing the Handbooks of Modern Statistical Methods. Apparently the series is popular as it is growing rapidly, with 13 volumes printed now and 7 announced. It is easy to understand why: the books are attractive in content, presentation and price. The present volume is no exception. The book has been edited by first-class experts, who also contributed a number of chapters. The book’s website gives a table of the contents of the 35 chapters. It documents the rich variety of subjects. … I can only recommend this book."—Paul Eilers, ISCB News, May 2017

• Part I: Introduction Chapter 1: Integration of Different Epidemiologic Perspectives and Applications to Spatial Epidemiology Sara Wagner Robb, Sarah E. Bauer, John E. Vena Chapter 2: Environmental Studies Mark J. Nieuwenhuijsen Chapter 3: Interpreting Clusters of Health Events Geoffrey Jacquez, Jared Aldstadt Chapter 4: Geographic Information Systems in Spatial Epidemiology and Public Health Robert Haining, Ravi Maheswaran Chapter 5: Ecological Modeling: General Issues Jon C. Wakefield, Theresa R. SmithPart II: Basic Methods Chapter 6: Case Event and Count Data Modeling Andrew B. Lawson Chapter 7: Bayesian Modeling and Inference Georgiana Onicescu, Andrew B. Lawson Chapter 8: Statistical Tests for Clustering and Surveillance Peter A. Rogerson, Geoffrey Jacquez Chapter 9: Scan Tests Inkyung Jung Chapter 10: Kernel Smoothing Methods Martin L. HazeltonPart III: Special Methods Chapter 11: Geostatistics in Small-Area Health Applications Patrick E. Brown Chapter 12: Splines in Disease Mapping Tomás Goicoa, Jaione Etxeberria, and María Dolores Ugarte Chapter 13: Quantile Regression for Epidemiological Applications Brian J. Reich Chapter 14: Focused Clustering: Statistical Analysis of Spatial Patterns of Disease around Putative Sources of Increased Risk Lance A. Waller, David C. Wheeler, Jeffrey M. Switchenko Chapter 15: Estimating the Health Impact of Air Pollution Fields Duncan Lee, Sujit K. Sahu Chapter 16: Data Assimilation for Environmental Pollution Fields Howard H. Chang Chapter 17: Spatial Survival Models Sudipto Banerjee Chapter 18: Spatial Longitudinal Analysis Andrew B. Lawson Chapter 19: Spatiotemporal Disease Mapping Andrew B. Lawson, Jungsoon Choi Chapter 20: Mixtures and Latent Structure in Spatial Epidemiology Md. Monir Hossain and Andrew B. Lawson Chapter 21: Bayesian Nonparametric Modeling for Disease Incidence Data Athanasios Kottas Chapter 22: Multivariate Spatial Models Sudipto BanerjeePart IV: Special Problems and Applications Chapter 23: Bayesian Variable Selection in Semiparametric and Nonstationary Geostatistical Models: An Application to Mapping Malaria Risk in Mali Federica Giardina, Nafomon Sogoba, Penelope Vounatsou Chapter 24: Computational Issues and R Packages for Spatial Data Analysis Marta Blangiardo, Michela Cameletti Chapter 25: The Role of Spatial Analysis in Risk-Based Animal Disease Management Kim B. Stevens, Dirk U. Pfeiffer Chapter 26: Infectious Disease Modelling Michael Höhle Chapter 27: Spatial Health Surveillance Ana Corberán-Vallet and Andrew B. Lawson Chapter 28: Cluster Modeling and Detection Andrew B. Lawson Chapter 29: Spatial Data Analysis for Health Services Research Brian Neelon Chapter 30: Spatial Health Survey Data Christel Faes, Yannick Vandendijck, Andrew B. Lawson Chapter 31: Graphical Modeling of Spatial Health Data Adrian Dobra Chapter 32: Smoothed ANOVA Modeling Miguel A. Martinez-Beneito, James S. Hodges, and Marc Marí-Dell’Olmo Chapter 33: Sociospatial Epidemiology: Segregation Sue C. Grady Chapter 34: Sociospatial Epidemiology: Residential History Analysis David C. Wheeler, Catherine A. Calder Chapter 35: Spatiotemporal Modeling of Preterm Birth Joshua L. Warren, Montserrat Fuentes, Amy H. Herring, Peter H. Langlois